Dental caries prevalence by sex in prehistory: magnitude and meaning
Bibliographic record
Abstract
Introduction The focus of this chapter is to determine if there is a significant relationship between sex and oral disease in human prehistory. The idea that dental caries prevalence may be etiologically complex and multifactorial in nature is not new (Mandel, 1979). Nevertheless, significant advances in understanding the mechanisms of cariogenesis continue (Featherstone, 1987, 2000), and the epidemiological study of dental caries continues to broaden, embracing geographically and culturally more diverse populations. Less widely appreciated is the frequently reported finding that females display worse dental health than males, especially in epidemiological studies of living populations (Haugejorden, 1996). While some anthropologists are well aware of the tendency for women to exhibit worse dental health than men (Larsen, 1998; Walker and Hewlett, 1990), a systematic global survey of sex differences in dental pathology in prehistory has not been conducted. The present meta-analysis tested for a gender bias in oral health by gathering, critically evaluating, and statistically summarizing data on sex differences in dental caries for a global sample of early historic and prehistoric skeletal series. A recent evaluation of the etiological role of saliva, sex hormones, and women's reproductive life histories highlighted sex differences in dental prevalence for the Guanches of Tenerife, in the Canary Islands (Lukacs and Largaespada, 2006). This meta-analysis of sex differences in caries prevalence derives directly from the first author's research in South Asia and the Canary Islands, which found that females' caries rates were consistently greater than that of males (Lukacs, 1996).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".